scholarly journals Comparison of Matched Patient Data for SSIs following Total Hip and Total Knee Arthoplasty: IPC Versus NSQIP Surveillance

2020 ◽  
Vol 41 (S1) ◽  
pp. s177-s178
Author(s):  
Jennifer Ellison ◽  
David Chakravorty ◽  
John Conly ◽  
Joseph Kim ◽  
Stacey Litvinchuk ◽  
...  
Keyword(s):  
Surgical Site Infections ◽  
Patient Data ◽  
Data Capture ◽  
Infection Prevention And Control ◽  
Data Sets ◽  
Surveillance Systems ◽  
Improvement Program ◽  
Total Hip ◽  
Future Work ◽  
Positive Agreement

Background: In Alberta, Canada, surgical site infections (SSIs) following total hip and knee replacements (THRs and TKRs) are reported using the infection prevention and control (IPC) surveillance system, which surveys all THRs and TKRs using the NHSN definitions; and the National Surgical Quality Improvement Program (NSQIP), which uses different definitions and sampling strategies. Deterministic matching of patient data from these sources was used to examine the overlap and discrepancies in SSI reporting. Methods: A retrospective multisite cohort study of IPC and NSQIP superficial, deep, and organ-space THR/TKR SSI data collected 30 days postoperatively from September 1, 2015, to March 31, 2018 was undertaken. To identify patients with procedures captured by both IPC and NSQIP, data were cleaned, duplicates removed, and patients matched 1:1 using year of birth, procedure facility, type, side, date, and time. Positive and negative agreement were assessed, and the Cohen κ values were calculated. The definitions and data capture methods used by both IPC and NSQIP were also compared. Results: There were 7,549 IPC and 2,037 NSQIP patients, respectively, with 1,798 matched patients: IPC (23.8%) and NSQIP (88.3%). Moreover, 17 SSIs were identified by both IPC and NSQIP, including 9 superficial and 8 complex by IPC and 6 superficial and 11 complex by NSQIP. Also, 7 SSIs were identified only by IPC, of which 5 were superficial, and 36 SSIs were identified only by NSQIP, of which 28 were superficial (positive agreement, 0.44; negative agreement, 0.99; κ = .43). Excluding superficial SSIs, 7 SSIs were identified by both IPC and NSQIP; 3 were identified only by IPC; and 12 were identified only by NSQIP (positive agreement, 0.48; negative agreement, 1.00; κ = 0.48). Conclusions: THR/TKR SSI rates reported by IPC and NSQIP were not comparable in this matched dataset. NSQIP identifies more superficial SSIs. Variations in data capture methods and definitions accounted for most of the discordance. Both surveillance systems are critically involved with improving patient outcomes following surgery. However, stakeholders need to be aware of these variations, and education should be provided to facilitate an understanding of the differences and their interpretation. Future work should explore other surgical procedures and larger data sets.Funding: NoneDisclosures: None

A comparison of surgical site infections following total hip replacement and total knee replacement surgeries identified by Infection Prevention and Control and the National Surgical Quality Improvement Program in Alberta, Canada

2021 ◽  
pp. 1-7
Author(s):  
Jennifer J. R. Ellison ◽  
Lesia R. Boychuk ◽  
David Chakravorty ◽  
A. Uma Chandran ◽  
John M. Conly ◽  
...  
Keyword(s):  
Quality Improvement ◽  
Prevention And Control ◽  
Infection Prevention ◽  
Surgical Site Infections ◽  
Infection Prevention And Control ◽  
Improvement Program ◽  
Surgical Quality ◽  
Total Hip ◽  
Total Knee ◽  
And Control

Abstract Objective: To understand how the different data collections methods of the Alberta Health Services Infection Prevention and Control Program (IPC) and the National Surgical Quality Improvement Program (NSQIP) are affecting reported rates of surgical site infections (SSIs) following total hip replacements (THRs) and total knee replacements (TKRs). Design: Retrospective cohort study. Setting: Four hospitals in Alberta, Canada. Patients: Those with THR or TKR surgeries between September 1, 2015, and March 31, 2018. Methods: Demographic information, complex SSIs reported by IPC and NSQIP were compared and then IPC and NSQIP data were matched with percent agreement and Cohen’s κ calculated. Statistical analysis was performed for age, gender and complex SSIs. A P value <.05 was considered significant. Results: In total, 7,549 IPC and 2,037 NSQIP patients were compared. The complex SSI rate for NSQIP was higher compared to IPC (THR: 1.19 vs 0.68 [P = .147]; TKR: 0.92 vs 0.80 [P = .682]). After matching, 7 SSIs were identified by both IPC and NSQIP; 3 were identified only by IPC, and 12 were identified only by NSQIP (positive agreement, 0.48; negative agreement, 1.0; κ = 0.48). Conclusions: Different approaches to monitor SSIs may lead to different results and trending patterns. NSQIP reports total SSI rates that are consistently higher than IPC. If systems are compared at any point in time, confidence on the data may be eroded. Stakeholders need to be aware of these variations and education provided to facilitate an understanding of differences and a consistent approach to SSI surveillance monitoring over time.

scholarly journals Reporting Surgical Site Infections (SSIs) Using Different Surveillance Systems— Complexity of Infection Matters

2020 ◽  
Vol 41 (S1) ◽  
pp. s372-s372
Author(s):  
Jennifer Ellison ◽  
Control ◽  
David Chakravorty ◽  
John Conly ◽  
Joseph Kim ◽  
...  
Keyword(s):  
Pearson Correlation ◽  
Surgical Site Infections ◽  
Data Sources ◽  
Infection Prevention And Control ◽  
Systematic Sampling ◽  
Surveillance Systems ◽  
Improvement Program ◽  
Icd 10 ◽  
And Control ◽  
Over Time

Background: In Alberta, Canada, surgical site infections (SSIs) following total hip (THR) and knee replacements (TKR) are reported using 2 data sources: infection prevention and control (IPC), which surveys all THR and TKR using NHSN definitions and the Canadian International Classification of Disease, Tenth Revision (ICD-10-CA) codes, and the National Surgical Quality Improvement Program (NSQIP), which uses a systematic sampling process that involves an 8-day cycle schedule, modified NHSN definitions and current procedural terminology (CPT) codes. We compared the similarities and discrepancies in THR/TKR SSI reporting. Methods: A retrospective multisite cohort study of IPC and NSQIP THR/TKR SSI data at 4 hospitals was performed. SSI data were collected between September 1, 2015, and March 31, 2018. Demographic information and complex and total SSIs reported by IPC and NSQIP were compared for both THR and TKR surgeries. To determine whether both data sources reported similar trends over time, total SSIs by quarter were compared. Univariate analyses using a t test for age and the χ2 test for gender for complex SSIs and total SSIs was performed. The Pearson correlation and the Shapiro-Wilk test were used to assess the THR and TKR trends between the 2 data sources. A P value of <.05 was considered significant. Results: Following the removal of duplicates and missing data, 7,549 IPC and 2,037 NSQIP patients, respectively, were compared. Age, gender, and other demographic parameters were not significantly different. Total THR and TKR SSIs per 100 procedures using NSQIP data were significantly higher than the same rates using IPC data: THR, 2.25 versus 0.92 (P < .05) and TKR, 3.43 versus 1.26 (P < .05). Both IPC and NSQIP data indicated increasing total THR SSI rates over time, but with different magnitudes (r = 0.658). For total TKR SSI, the IPC rate decreased, whereas the NSQIP rate increased over the same period (r = 0.374). When superficial SSIs were excluded, the rates reported between IPC and NSQIP data by hospital and by procedure type were more comparable, with trends toward higher rates reported by NSQIP for THR than for TKR: THR, 1.19 versus 0.68 (P = 0.15) and TKR, 0.92 versus 0.80 (P = .68). Conclusions: Different approaches used to monitor SSIs following surgeries may lead to different results and trend patterns. NSQIP reports total SSI rates that are significantly higher than the IPC Alberta orthopedic population predominantly as a result of increased identification of superficial SSIs. Because the diagnosis of superficial SSIs may be less reliable, SSI reporting should focus on complex infections.Funding: NoneDisclosures: None

scholarly journals Surgical site infection metrics: Dissecting the differences between the National Health and Safety Network and the National Surgical Quality Improvement Program

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Alaia M. M. Christensen ◽  
Karen Dowler ◽  
Shira Doron
Keyword(s):  
Quality Improvement ◽  
Health And Safety ◽  
Surgical Site Infections ◽  
Cost Of Care ◽  
Quality Improvement Program ◽  
Surveillance Systems ◽  
Improvement Program ◽  
Surgical Quality ◽  
National Healthcare Safety Network ◽  
Hospital Acquired

Abstract Surgical site infections (SSIs) are associated with readmissions, reoperations, increased cost of care, and overall morbidity and mortality risk. The National Healthcare Safety Network (NHSN) and the National Surgical Quality Improvement Program (NSQIP) have developed an array of metrics to monitor hospital-acquired complications. The only metric collected by both is SSI, but performance as benchmarked against peer hospitals is often discordant between the 2 systems. In this commentary, we outline the differences between these 2 surveillance systems as they relate to this potential for discordance.

Incidence, Causes, and Timing of 30-Day Readmission following Total Hip Arthroplasty

2019 ◽  
Vol 03 (03) ◽  
pp. 118-123
Author(s):  
Gannon L. Curtis ◽  
Michael Jawad ◽  
Linsen T. Samuel ◽  
Carlos A. Higuera-Rueda ◽  
Bryan E. Little ◽  
...  
Keyword(s):  
Total Hip Arthroplasty ◽  
Hip Arthroplasty ◽  
Linear Regression Analysis ◽  
Surgical Site Infections ◽  
Cardiovascular Complications ◽  
Wound Complications ◽  
Improvement Program ◽  
Gastrointestinal Complications ◽  
Total Hip ◽  
Common Causes

AbstractUnplanned readmissions are associated with increased financial burdens. It is important to understand why they occur and how to reduce them. This study identifies incidences, trends, causes, and timing of 30-day readmissions after total hip arthroplasty (THA). Primary THA cases from 2012 to 2016 in the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) database were identified (n = 122,451). Fractures (n = 3,990), nonelective surgery (n = 1,715), and bilateral THA (n = 730) were excluded, leaving 116,016 cases. Linear regression analysis determined readmission trends overtime. The readmission rate after THA from 2012 to 16 was 3.32%, which significantly decreased during this time (p = 0.022). The top five causes of readmission included musculoskeletal complications (14.8%), deep surgical site infections (SSI; 11.1%), non-SSI infections (10.8%), gastrointestinal complications (GI; 7.5%), and cardiovascular complications (CV; 7.0%). The most common cause of readmission during week 1 was non-SSI infections (13.0%), week 2 was musculoskeletal complications (16%), week 3 was deep SSI (18.4%), and week 4 was deep SSI (18.6%). Causes of readmission that significantly decreased (p < 0.05) from week 1 to 4 include CV complications, GI complications, non-SSI infections, pain, and respiratory complications. In contrast, causes that significantly increased during this time included deep SSI, prosthesis complications, superficial SSI, and wound complications. Readmissions following THA significantly declined from 2012 to 2016. The most common causes of readmission were musculoskeletal complications, deep SSI, non-SSI infections, GI complications, and CV complications. Interestingly, the most common causes of readmission changed from week to week. These findings may help to develop policies to prevent readmissions following THA.

scholarly journals Validation of an algorithm for semiautomated surveillance to detect deep surgical site infections after primary total hip or knee arthroplasty—A multicenter study

2020 ◽  
Vol 42 (1) ◽  
pp. 69-74
Author(s):  
Janneke D. M. Verberk ◽  
Stephanie M. van Rooden ◽  
Mayke B. G. Koek ◽  
David J. Hetem ◽  
Annelies E. Smilde ◽  
...  
Keyword(s):  
Knee Arthroplasty ◽  
High Probability ◽  
Surgical Site Infections ◽  
Data Interpretation ◽  
Routine Care ◽  
Successful Implementation ◽  
Subjective Data ◽  
Essential Information ◽  
Total Hip ◽  
Healthcare Associated

AbstractObjective:Surveillance of healthcare-associated infections is often performed by manual chart review. Semiautomated surveillance may substantially reduce workload and subjective data interpretation. We assessed the validity of a previously published algorithm for semiautomated surveillance of deep surgical site infections (SSIs) after total hip arthroplasty (THA) or total knee arthroplasty (TKA) in Dutch hospitals. In addition, we explored the ability of a hospital to automatically select the patients under surveillance.Design:Multicenter retrospective cohort study.Methods:Hospitals identified patients who underwent THA or TKA either by procedure codes or by conventional surveillance. For these patients, routine care data regarding microbiology results, antibiotics, (re)admissions, and surgeries within 120 days following THA or TKA were extracted from electronic health records. Patient selection was compared with conventional surveillance and patients were retrospectively classified as low or high probability of having developed deep SSI by the algorithm. Sensitivity, positive predictive value (PPV), and workload reduction were calculated and compared to conventional surveillance.Results:Of 9,554 extracted THA and TKA surgeries, 1,175 (12.3%) were revisions, and 8,378 primary surgeries remained for algorithm validation (95 deep SSIs, 1.1%). Sensitivity ranged from 93.6% to 100% and PPV ranged from 55.8% to 72.2%. Workload was reduced by ≥98%. Also, 2 SSIs (2.1%) missed by the algorithm were explained by flaws in data selection.Conclusions:This algorithm reliably detects patients with a high probability of having developed deep SSI after THA or TKA in Dutch hospitals. Our results provide essential information for successful implementation of semiautomated surveillance for deep SSIs after THA or TKA.

MondoA Drives B-ALL Malignancy through Enhanced Adaptation to Metabolic Stress

Blood ◽  
2021 ◽  
Author(s):  
Alexandra Sipol ◽  
Erik Hameister ◽  
Busheng Xue ◽  
Julia Hofstetter ◽  
Maxim Barenboim ◽  
...  
Keyword(s):  
Stress Responses ◽  
Lymphoblastic Leukemia ◽  
Metabolic Stress ◽  
Tca Cycle ◽  
Underlying Mechanism ◽  
Patient Data ◽  
Data Sets ◽  
Dependent Manner

Cancer cells are in most instances characterized by rapid proliferation and uncontrolled cell division. Hence, they must adapt to proliferation-induced metabolic stress through intrinsic or acquired anti-metabolic stress responses to maintain homeostasis and survival. One mechanism to achieve this is to reprogram gene expression in a metabolism-dependent manner. MondoA (also known as MLXIP), a member of the MYC interactome, has been described as an example of such a metabolic sensor. However, the role of MondoA in malignancy is not fully understood and the underlying mechanism in metabolic responses remains elusive. By assessing patient data sets we found that MondoA overexpression is associated with a worse survival in pediatric common acute lymphoblastic leukemia (B-ALL). Using CRISPR/Cas9 and RNA interference approaches, we observed that MondoA depletion reduces transformational capacity of B-ALL cells in vitro and dramatically inhibits malignant potential in an in vivo mouse model. Interestingly, reduced expression of MondoA in patient data sets correlated with enrichment in metabolic pathways. The loss of MondoA correlated with increased tricarboxylic acid (TCA) cycle activity. Mechanistically, MondoA senses metabolic stress in B-ALL cells by restricting oxidative phosphorylation through reduced PDH activity. Glutamine starvation conditions greatly enhance this effect and highlight the inability to mitigate metabolic stress upon loss of MondoA in B-ALL. Our findings give a novel insight into the function of MondoA in pediatric B-ALL and support the notion that MondoA inhibition in this entity offers a therapeutic opportunity and should be further explored.

scholarly journals Kurator: Tools for Improving Fitness for Use of Biodiversity Data.

2018 ◽  
Vol 2 ◽  
pp. e26539 ◽  
Author(s):  
Paul J. Morris ◽  
James Hanken ◽  
David Lowery ◽  
Bertram Ludäscher ◽  
James Macklin ◽  
...  
Keyword(s):  
Data Quality ◽  
Early Stage ◽  
Data Capture ◽  
Data Sets ◽  
Biodiversity Data ◽  
Skill Levels ◽  
Use Of Data ◽  
Data Life Cycle ◽  
Wide Range ◽  
Original Observation

As curators of biodiversity data in natural science collections, we are deeply concerned with data quality, but quality is an elusive concept. An effective way to think about data quality is in terms of fitness for use (Veiga 2016). To use data to manage physical collections, the data must be able to accurately answer questions such as what objects are in the collections, where are they and where are they from. Some research uses aggregate data across collections, which involves exchange of data using standard vocabularies. Some research uses require accurate georeferences, collecting dates, and current identifications. It is well understood that the costs of data capture and data quality improvement increase with increasing time from the original observation. These factors point towards two engineering principles for software that is intended to maintain or enhance data quality: build small modular data quality tests that can be easily assembled in suites to assess the fitness of use of data for some particular need; and produce tools that can be applied by users with a wide range of technical skill levels at different points in the data life cycle. In the Kurator project, we have produced code (e.g. Wieczorek et al. 2017, Morris 2016) which consists of small modules that can be incorporated into data management processes as small libraries that address particular data quality tests. These modules can be combined into customizable data quality scripts, which can be run on single computers or scalable architecture and can be incorporated into other software, run as command line programs, or run as suites of canned workflows through a web interface. Kurator modules can be integrated into early stage data capture applications, run to help prepare data for aggregation by matching it to standard vocabularies, be run for quality control or quality assurance on data sets, and can report on data quality in terms of a fitness-for-use framework (Veiga et al. 2017). One of our goals is simple tests usable by anyone anywhere.

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